HSP-MFL: A High-level Semantic Property driven Multi-task Feature Learning Network for unsupervised person Re-ID

Unsupervised person re-identification aims to distinguish different pedestrians from discriminative representations on the basis of unlabeled data. Currently, most unsupervised Re-ID approaches explore visual representations to generate pseudo-labels for model’s training, which may suffer from background noise and semantic loss. To tackle this problem, this paper proposes a High-level Semantic Property driven Multi-task Feature Learning Network (HSP-MFL) to firstly introduce three high-level semantic properties for unsupervised person Re-ID. Technically, we design a novel Multiple Feature Fusion Module (MFFM) to deeply explore the complex correlation among multiple semantic and visual features to capture the discriminative feature cues, as well as a multi-task training scheme to generate robust fusion features. The architecture is quite simple and does not consume extra labeling costs. Extensive experiments on three datasets demonstrate that both high-level semantic properties and multi-task learning are effective in performance improvement, yielding SOTA mAPs for unsupervised person Re-ID.     

多姿态图像生成的行人重识别算法研究

不同行人的高度相似性以及相同行人外观姿态的差异性,使得不同摄像头下的行人重识别面临严峻的挑战。生成对抗网络可以合成新的图像,被认为是解决行人姿态变化的主要技术手段。提出一种基于多姿态图像生成的行人重识别算法,利用生成对抗网络生成不同姿态的行人图像,通过归一化消除姿态的影响,从而大幅度提升行人重识别的整体性能。该行人重识别算法包括多姿态行人图像生成、不同姿态的行人特征提取与融合、距离度量和重排序三部分内容。在Market-1501数据集和DukeMTMC-ReID数据集上的实验证实了所提出算法的有效性,通过与state-of-the-art行人重识别方法比较,展示了多姿态图像生成方法在行人重识别任务中的优越性,同时表明生成行人图像的特征与原始图像的特征是相互补充的。     

素描图像指导的换装行人重识别

目的 传统行人重识别方法提取到的特征中包含大量的衣物信息,在换装行人重识别问题中,依靠衣物相关的信息难以准确判别行人身份,使模型性能显著下降;虽然一些方法从轮廓图像中提取行人的体型信息以增强行人特征,但轮廓图像的质量参差不齐,鲁棒性差。针对这些问题,本文提出一种素描图像指导的换装行人重识别方法。方法 首先,本文认为相对于轮廓图像,素描图像能够提供更鲁棒、更精准的行人体型信息,因此本文使用素描图像提取行人的体型信息,并将其融入表观特征以获取完备的行人特征。然后,提出一个基于素描图像的衣物无关权重指导模块,进一步使用素描图像中的衣物位置信息指导表观特征的提取过程,从而减少表观特征中的衣物信息,增强表观特征的判别力。结果 在LTCC(long-term cloth changing)和PRCC(person re-identification under moderate clothing change)两个常用换装行人数据集上,本文方法与最先进的方法进行了对比。相较于先进方法,在LTCC和PRCC数据集上,本文方法在Rank-1性能指标上分别提高了6.5%和3.9%。实验结果表明,素描图像...     

换装行人重识别研究进展

行人重识别旨在建立目标行人在多个无交叉覆盖监控区域间的身份联系,在智慧城市、司法侦查和监控安全等领域具有重要应用价值。传统行人重识别方法针对短时间跨度场景,依赖行人外观特征的稳定不变性,旨在克服光照差异、视角变化和物体遮挡等挑战。与之不同,换装行人重识别针对长时间跨度场景,除受限于上述挑战还面临换装带来的外观变化问题,是近几年的一个研究难点和热点。围绕换装行人重识别,本文从数据集和解决方法两个方面综述国内外研究进展,探讨面临的挑战和难点。首先,梳理并比较了当前针对换装行人重识别的数据集,从采集方式、行人及样本数量等方面分析其挑战性和面临的局限性。然后,在简单回顾换装行人重识别发展历史的基础上,将其归纳为基于非视觉传感器的方法和基于视觉相机的方法两类。针对基于非视觉传感器的方法,介绍了深度传感器、射频信号等在换装行人重识别中的应用。针对基于视觉相机的方法,详细阐述了基于显式特征设计与提取的方法、基于特征解耦的方法和基于隐式数据驱动自适应学习的方法。在此基础上,探讨了当前换装行人重识别面临的问题并展望未来的发展趋势,旨在为相关研究提供参考。     

基于相机感知距离矩阵的无监督行人重识别

在跨场景、跨设备的行人重识别中虽然增加了可利用的行人数据,但由于行人姿态不同、部分遮挡现象,难以避免引入样本噪声,在聚类过程中易生成错误的伪标签,造成标签噪声,影响模型的优化。为减弱噪声影响,应用相机感知的距离矩阵对抗相机偏移引起的样本噪声问题,利用对噪声鲁棒的动态对称对比损失减少标签噪声,提出基于相机感知距离矩阵的无监督行人重识别算法。在聚类前通过更改度量行人特征相似度的距离矩阵,利用相机感知距离矩阵来增强类内距离度量准确性,减少由于拍摄视角不同对聚类效果造成的负面影响。同时,结合噪声标签学习方法,进行损失设计,提出动态对称对比损失函数,联合损失训练,不断精炼伪标签。在DukeMTMC-reID和Market-1501两个数据集上进行实验,验证了提出方法的有效性。     

Visible–infrared person re-identification based on key-point feature extraction and optimization

Feature extraction for visible–infrared person re-identification (VI-ReID) is challenging because of the cross-modality discrepancy in the images taken by different spectral cameras. Most of the existing VI-ReID methods often ignore the potential relationship between features. In this paper, we intend to transform low-order person features into high-order graph features, and make full use of the hidden information between person features. Therefore, we propose a multi-hop attention graph convolution network (MAGC) to extract robust person joint feature information using residual attention mechanism while reducing the impact of environmental noise. The transfer of higher order graph features within MAGC enables the network to learn the hidden relationship between features. We also introduce the self-attention semantic perception layer (SSPL) which can adaptively select more discriminant features to further promote the transmission of useful information. The experiments on VI-ReID datasets demonstrate its effectiveness.     

Fine-grained-based multi-feature fusion for occluded person re-identification

Many previous occluded person re-identification(re-ID) methods try to use additional clues (pose estimation or semantic parsing models) to focus on non-occluded regions. However, these methods extremely rely on the performance of additional clues and often capture pedestrian features by designing complex modules. In this work, we propose a simple Fine-Grained Multi-Feature Fusion Network (FGMFN) to extract discriminative features, which is a dual-branch structure consisting of global feature branch and partial feature branch. Firstly, we utilize a chunking strategy to extract multi-granularity features to make the pedestrian information contained in it more comprehensive. Secondly, a spatial transformer network is introduced to localize the pedestrian’s upper body, and then introduce a relation-aware attention module to explore the fine-grained information. Finally, we fuse the features obtained from the two branches to obtain a more robust pedestrian representation. Extensive experiments verify the effectiveness of our method under the occlusion scenario.     

基于负熵的人员脚步震动信号检测识别算法

提出一种基于震动信号的人员脚步检测识别算法,该算法根据信息论中的负熵概念,采用高阶累积量的负熵近似计算方法.仿真与实测结果证明,与一般的模式识别算法相比,该算法具有三个重要的优点,包括环境适应性强、识别准确率高和运算量小.这些优点使得该算法更适用于能量受限、随机自组的无线传感器网络,能够在野外环境下准确、简单的检测识别人员脚步震动信号.     

Using a Visual Attention Model to Improve Gaze Tracking Systems in Interactive 3D Applications

This paper introduces the use of a visual attention model to improve the accuracy of gaze tracking systems. Visual attention models simulate the selective attention part of the human visual system. For instance, in a bottom‐up approach, a saliency map is defined for the image and gives an attention weight to every pixel of the image as a function of its colour, edge or intensity. Our algorithm uses an uncertainty window, defined by the gaze tracker accuracy, and located around the gaze point given by the tracker. Then, using a visual attention model, it searches for the most salient points, or objects, located inside this uncertainty window, and determines a novel, and hopefully, better gaze point. This combination of a gaze tracker together with a visual attention model is considered as the main contribution of the paper. We demonstrate the promising results of our method by presenting two experiments conducted in two different contexts: (1) a free exploration of a visually rich 3D virtual environment without a specific task, and (2) a video game based on gaze tracking involving a selection task. Our approach can be used to improve real‐time gaze tracking systems in many interactive 3D applications such as video games or virtual reality applications. The use of a visual attention model can be adapted to any gaze tracker and the visual attention model can also be adapted to the application in which it is used.     

A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: A railway risk management system

Risk management is becoming increasingly important for railway companies in order to safeguard their passengers and employees while improving safety and reducing maintenance costs. However, in many circumstances, the application of probabilistic risk analysis tools may not give satisfactory results because the risk data are incomplete or there is a high level of uncertainty involved in the risk data. This article presents the development of a risk management system for railway risk analysis using fuzzy reasoning approach and fuzzy analytical hierarchy decision making process. In the system, fuzzy reasoning approach (FRA) is employed to estimate the risk level of each hazardous event in terms of failure frequency, consequence severity and consequence probability. This allows imprecision or approximate information in the risk analysis process. Fuzzy analytical hierarchy process (fuzzy-AHP) technique is then incorporated into the risk model to use its advantage in determining the relative importance of the risk contributions so that the risk assessment can be progressed from hazardous event level to hazard group level and finally to railway system level. This risk assessment system can evaluate both qualitative and quantitative risk data and information associated with a railway system effectively and efficiently, which will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management of railway systems and set safety standards. A case study on risk assessment of shunting at Hammersmith depot is used to illustrate the application of the proposed risk assessment system.